Image analysis for decision making is an important component of many tasks such as medical diagnosis, industrial applications and satellite image interpretation. Human observers rely on their own and others' acquired knowledge and experience to annotate and interpret images. It is important to train novice observers adequately so that they can perform these tasks competently. An important component of this training is the availability of human experts to train novices. However it is difficult to find adequate number of qualified experts to impart training to others.
An example case in point is retinal image analysis for pathology detection. Diagnosis and management of retinal conditions such as diabetic retinopathy (DR) or Age related macular Degeneration (AMD) is important as they are one of the leading causes of blindness in the world. DR is a complication of diabetes and AMD is a complication of advancing age. With the increasing number of such patients around the world, it is important to have clinicians who are adequately trained to detect incidence of DR and AMD, and recommend appropriate action. Training ophthalmologists is a resource intensive procedure that requires considerable time and effort from experienced ophthalmologists and clinicians. Such training may be relatively easier to impart in urban clinics which has higher number of trained clinicians. However, such resources are not available in remote areas and therefore patients residing in remote areas are at a severe disadvantage due to lack of access to clinicians and other facilities.
Recently tele-ophthalmology has emerged as a possible solution where retinal scans are taken at remote locations using relatively inexpensive hardware and are transmitted to a central server to be assessed by ophthalmologists. Once the assessment is completed a report is sent back to the remote clinic. Commonly, tele-ophthalmology programs are monitored by inexperienced clinicians who may not be trained in severity assessment but have basic training in operating the equipment. Although this approach has improved the access to clinics for people in remote locations it also suffers from the following limitations:
Considerable time is spent from the time a patient's image is acquired to getting a report from an expert. Due to this delay the following incidents are frequently observed: a) the patient does not return for a follow up check; and b) retinal conditions may change rapidly for the worse. The consistency of the grading is not guaranteed as images from the same location or of the same patient may not be analyzed by the same expert.
Referring to the ophthalmology case example, irrespective of the fact whether the clinician is in a urban or remote setting, the inventors in this disclosure have recognized that training ophthalmologists requires the following of a set of formal guidelines. For instance, the inventors in this disclosure have recognized that the above issues can be addressed to a significant degree if there exists a training module for ophthalmologists that can performs the functions such as: 1) Assists operators in learning to identify the relevant patterns necessary to detect retinal pathologies without direct supervision of experts; 2) Suggests locations of interest (pathologies) to the less experienced grader using automatic algorithms to ensure consistency; 3) Provides feedback on the operators' proficiency and identify areas where further training is required, by comparing their detections with the detections of an automatic algorithm. The inventors in this disclose have also recognized that such a system should output consistent results in order to reduce bias due to subjectivity.